Overview

Dataset statistics

Number of variables6
Number of observations653
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.6 KiB
Average record size in memory54.2 B

Variable types

Numeric6

Alerts

SPOT_LO is highly overall correlated with SPOT_ALHigh correlation
SPOT_AL is highly overall correlated with SPOT_LOHigh correlation
GRDNT_RT is highly overall correlated with GRDNT_VALHigh correlation
GRDNT_VAL is highly overall correlated with GRDNT_RTHigh correlation
SPOT_LO has unique valuesUnique
SPOT_LA has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:53:52.793182
Analysis finished2024-03-13 12:53:59.425841
Duration6.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SPOT_LO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct653
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.32051
Minimum129.32038
Maximum129.32063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-13T21:53:59.538891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.32038
5-th percentile129.32042
Q1129.32046
median129.3205
Q3129.32055
95-th percentile129.3206
Maximum129.32063
Range0.0002576866
Interquartile range (IQR)9.00619 × 10-5

Descriptive statistics

Standard deviation5.7154686 × 10-5
Coefficient of variation (CV)4.4196151 × 10-7
Kurtosis-0.82099787
Mean129.32051
Median Absolute Deviation (MAD)4.48401 × 10-5
Skewness0.070397478
Sum84446.29
Variance3.2666581 × 10-9
MonotonicityNot monotonic
2024-03-13T21:53:59.751945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.3204210284 1
 
0.2%
129.3205549753 1
 
0.2%
129.3205324598 1
 
0.2%
129.3205437367 1
 
0.2%
129.3205550136 1
 
0.2%
129.3205662904 1
 
0.2%
129.3205775673 1
 
0.2%
129.3205888441 1
 
0.2%
129.320600121 1
 
0.2%
129.3206113979 1
 
0.2%
Other values (643) 643
98.5%
ValueCountFrequency (%)
129.3203758062 1
0.2%
129.3203758444 1
0.2%
129.3203869685 1
0.2%
129.3203870067 1
0.2%
129.3203870449 1
0.2%
129.3203870831 1
0.2%
129.3203871213 1
0.2%
129.3203966409 1
0.2%
129.3203981308 1
0.2%
129.320398169 1
0.2%
ValueCountFrequency (%)
129.3206334928 1
0.2%
129.3206229806 1
0.2%
129.3206229423 1
0.2%
129.3206229041 1
0.2%
129.3206226747 1
0.2%
129.3206226365 1
0.2%
129.3206225983 1
0.2%
129.3206222924 1
0.2%
129.3206222542 1
0.2%
129.320622216 1
0.2%

SPOT_LA
Real number (ℝ)

UNIQUE 

Distinct653
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.287278
Minimum37.28709
Maximum37.287523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-13T21:53:59.972178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.28709
5-th percentile37.287099
Q137.287172
median37.287271
Q337.287379
95-th percentile37.287478
Maximum37.287523
Range0.0004329497
Interquartile range (IQR)0.000207144

Descriptive statistics

Standard deviation0.00012176765
Coefficient of variation (CV)3.2656621 × 10-6
Kurtosis-1.0994778
Mean37.287278
Median Absolute Deviation (MAD)9.92653 × 10-5
Skewness0.18622588
Sum24348.593
Variance1.4827361 × 10-8
MonotonicityStrictly decreasing
2024-03-13T21:54:00.264264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2875231083 1
 
0.2%
37.2871983731 1
 
0.2%
37.2872074445 1
 
0.2%
37.2872074139 1
 
0.2%
37.2872073834 1
 
0.2%
37.2872073528 1
 
0.2%
37.2872073223 1
 
0.2%
37.2872072918 1
 
0.2%
37.2872072612 1
 
0.2%
37.2872072307 1
 
0.2%
Other values (643) 643
98.5%
ValueCountFrequency (%)
37.2870901586 1
0.2%
37.2870901891 1
0.2%
37.2870902197 1
0.2%
37.2870902502 1
0.2%
37.2870902807 1
0.2%
37.2870903113 1
0.2%
37.2870903418 1
0.2%
37.2870903723 1
0.2%
37.2870904029 1
0.2%
37.2870904334 1
0.2%
ValueCountFrequency (%)
37.2875231083 1
0.2%
37.2875230778 1
0.2%
37.2875230473 1
0.2%
37.2875141592 1
0.2%
37.2875141286 1
0.2%
37.2875140981 1
0.2%
37.2875140676 1
0.2%
37.287514037 1
0.2%
37.28750521 1
0.2%
37.2875051794 1
0.2%

SPOT_AL
Real number (ℝ)

HIGH CORRELATION 

Distinct637
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2543323
Minimum-3.439
Maximum17.186
Zeros1
Zeros (%)0.2%
Negative77
Negative (%)11.8%
Memory size5.9 KiB
2024-03-13T21:54:00.495442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.439
5-th percentile-0.529
Q10.634
median3.055
Q36.854
95-th percentile13.1988
Maximum17.186
Range20.625
Interquartile range (IQR)6.22

Descriptive statistics

Standard deviation4.3781706
Coefficient of variation (CV)1.0291087
Kurtosis-0.19094792
Mean4.2543323
Median Absolute Deviation (MAD)2.713
Skewness0.86656075
Sum2778.079
Variance19.168378
MonotonicityNot monotonic
2024-03-13T21:54:00.669761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.121 2
 
0.3%
2.404 2
 
0.3%
1.211 2
 
0.3%
1.219 2
 
0.3%
-0.895 2
 
0.3%
-0.118 2
 
0.3%
0.559 2
 
0.3%
1.059 2
 
0.3%
0.341 2
 
0.3%
1.008 2
 
0.3%
Other values (627) 633
96.9%
ValueCountFrequency (%)
-3.439 1
0.2%
-2.877 1
0.2%
-2.061 1
0.2%
-1.707 1
0.2%
-1.397 1
0.2%
-1.216 1
0.2%
-1.15 1
0.2%
-1.108 1
0.2%
-1.107 1
0.2%
-1.075 1
0.2%
ValueCountFrequency (%)
17.186 1
0.2%
16.593 1
0.2%
16.217 1
0.2%
16.131 1
0.2%
16.054 1
0.2%
15.897 1
0.2%
15.588 1
0.2%
15.313 1
0.2%
15.197 1
0.2%
15.084 1
0.2%

GRDNT_RT
Real number (ℝ)

HIGH CORRELATION 

Distinct646
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.47222
Minimum7.09
Maximum711.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-13T21:54:00.853102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.09
5-th percentile25.94
Q165.48
median105.06
Q3159.31
95-th percentile268.144
Maximum711.31
Range704.22
Interquartile range (IQR)93.83

Descriptive statistics

Standard deviation89.642819
Coefficient of variation (CV)0.72601609
Kurtosis11.098696
Mean123.47222
Median Absolute Deviation (MAD)44.3
Skewness2.5255497
Sum80627.36
Variance8035.835
MonotonicityNot monotonic
2024-03-13T21:54:01.039440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.24 2
 
0.3%
30.35 2
 
0.3%
91.17 2
 
0.3%
99.35 2
 
0.3%
79.31 2
 
0.3%
67.57 2
 
0.3%
119.13 2
 
0.3%
72.46 1
 
0.2%
108.39 1
 
0.2%
120.79 1
 
0.2%
Other values (636) 636
97.4%
ValueCountFrequency (%)
7.09 1
0.2%
7.33 1
0.2%
8.21 1
0.2%
8.92 1
0.2%
9.14 1
0.2%
10.01 1
0.2%
10.79 1
0.2%
10.82 1
0.2%
12.67 1
0.2%
12.78 1
0.2%
ValueCountFrequency (%)
711.31 1
0.2%
705.77 1
0.2%
692.15 1
0.2%
644.06 1
0.2%
576.1 1
0.2%
559.22 1
0.2%
490.16 1
0.2%
424.82 1
0.2%
423.5 1
0.2%
420.14 1
0.2%

GRDNT_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct624
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.940582
Minimum4.06
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-13T21:54:01.243370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.06
5-th percentile14.544
Q133.22
median46.41
Q357.88
95-th percentile69.546
Maximum82
Range77.94
Interquartile range (IQR)24.66

Descriptive statistics

Standard deviation16.732188
Coefficient of variation (CV)0.37231802
Kurtosis-0.49461853
Mean44.940582
Median Absolute Deviation (MAD)12.27
Skewness-0.28924205
Sum29346.2
Variance279.96613
MonotonicityNot monotonic
2024-03-13T21:54:01.439457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.99 3
 
0.5%
57.09 2
 
0.3%
46.6 2
 
0.3%
65.56 2
 
0.3%
16.88 2
 
0.3%
66.55 2
 
0.3%
51.44 2
 
0.3%
42.36 2
 
0.3%
46.73 2
 
0.3%
38.42 2
 
0.3%
Other values (614) 632
96.8%
ValueCountFrequency (%)
4.06 1
0.2%
4.19 1
0.2%
4.7 1
0.2%
5.1 1
0.2%
5.22 1
0.2%
5.72 1
0.2%
6.16 1
0.2%
6.17 1
0.2%
7.22 1
0.2%
7.28 1
0.2%
ValueCountFrequency (%)
82.0 1
0.2%
81.94 1
0.2%
81.78 1
0.2%
81.17 1
0.2%
80.15 1
0.2%
79.86 1
0.2%
78.47 1
0.2%
76.75 1
0.2%
76.71 1
0.2%
76.61 1
0.2%

SLANT_DRC
Real number (ℝ)

Distinct640
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.88643
Minimum1.07
Maximum359.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-13T21:54:01.656289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.07
5-th percentile25.728
Q164.25
median88.58
Q3138.7
95-th percentile244.82
Maximum359.68
Range358.61
Interquartile range (IQR)74.45

Descriptive statistics

Standard deviation71.661374
Coefficient of variation (CV)0.65812951
Kurtosis2.8322758
Mean108.88643
Median Absolute Deviation (MAD)30.5
Skewness1.5859705
Sum71102.84
Variance5135.3525
MonotonicityNot monotonic
2024-03-13T21:54:01.846863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.61 2
 
0.3%
74.64 2
 
0.3%
62.49 2
 
0.3%
64.13 2
 
0.3%
53.79 2
 
0.3%
80.01 2
 
0.3%
58.74 2
 
0.3%
70.76 2
 
0.3%
87.64 2
 
0.3%
38.39 2
 
0.3%
Other values (630) 633
96.9%
ValueCountFrequency (%)
1.07 1
0.2%
1.9 1
0.2%
4.48 1
0.2%
4.67 1
0.2%
7.37 1
0.2%
8.31 1
0.2%
8.63 1
0.2%
8.79 1
0.2%
8.99 1
0.2%
9.94 1
0.2%
ValueCountFrequency (%)
359.68 1
0.2%
358.18 1
0.2%
358.13 1
0.2%
357.41 1
0.2%
356.78 1
0.2%
356.28 1
0.2%
356.23 1
0.2%
354.88 1
0.2%
354.59 1
0.2%
354.12 1
0.2%

Interactions

2024-03-13T21:53:57.804870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:53.195509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:54.138242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:55.039799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:55.926887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:56.869006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:57.971647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:53.374110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:54.293860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:55.170449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:56.100336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:57.002877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:58.139498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:53.524452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:54.436368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:55.306178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:56.250544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:57.146236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:58.285728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:53.675325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:54.565539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:55.446034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:56.384951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:57.281980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:58.859784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:53.809769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:54.722536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:55.642268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:56.543059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:57.436621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:59.026563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:53.965613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:54.879389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:55.787308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:56.717247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:57.604922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:54:02.008173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
SPOT_LO1.0000.5940.6850.4100.3250.242
SPOT_LA0.5941.0000.5420.4070.4500.516
SPOT_AL0.6850.5421.0000.0000.0760.312
GRDNT_RT0.4100.4070.0001.0000.9400.475
GRDNT_VAL0.3250.4500.0760.9401.0000.540
SLANT_DRC0.2420.5160.3120.4750.5401.000
2024-03-13T21:54:02.146158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
SPOT_LO1.000-0.357-0.5500.0200.0200.075
SPOT_LA-0.3571.000-0.400-0.170-0.170-0.208
SPOT_AL-0.550-0.4001.0000.0900.0900.081
GRDNT_RT0.020-0.1700.0901.0001.000-0.154
GRDNT_VAL0.020-0.1700.0901.0001.000-0.154
SLANT_DRC0.075-0.2080.081-0.154-0.1541.000

Missing values

2024-03-13T21:53:59.206423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:53:59.367131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
0129.32042137.2875231.12872.4635.9387.06
1129.32043237.287523-0.01859.7330.8589.54
2129.32044437.287523-0.05266.9633.899.5
3129.32039837.2875142.10175.0936.9102.22
4129.3204137.2875141.652190.4362.345.22
5129.32042137.2875141.147116.8149.4333.94
6129.32043237.2875140.566126.4251.6525.94
7129.32044437.287514-0.477145.9155.5834.16
8129.32037637.2875055.466111.6848.1648.26
9129.32038737.2875053.305576.180.1527.88
SPOT_LOSPOT_LASPOT_ALGRDNT_RTGRDNT_VALSLANT_DRC
643129.32048737.2870911.763130.2252.4875.22
644129.32049837.2870910.839137.9754.0777.03
645129.32050937.287099.777140.7154.670.76
646129.32052137.287098.844121.0950.4558.38
647129.32053237.287097.722139.1154.2982.7
648129.32054337.287096.558129.3952.383.95
649129.32055537.287095.451111.348.0674.6
650129.32056637.287094.703103.0845.8759.53
651129.32057737.287094.215144.8755.3884.66
652129.32058837.287093.701148.6356.0786.54